Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Efficient architectures for multidimensional discrete transforms in image and video processing applications

PhD ThesisThis thesis introduces new image compression algorithms, their related architectures and data transforms architectures. The proposed architectures consider the current hardware architectures concerns, such as power consumption, hardware usage, memory requirement, computation time and output accuracy. These concerns and problems are crucial in multidimensional image and video processing applications. This research is divided into three image and video processing related topics: low complexity non-transform-based image compression algorithms and their architectures, architectures for multidimensional Discrete Cosine Transform (DCT); and architectures for multidimensional Discrete Wavelet Transform (DWT). The proposed architectures are parameterised in terms of wordlength, pipelining and input data size. Taking such parameterisation into account, efficient non-transform based and low complexity image compression algorithms for better rate distortion performance are proposed. The proposed algorithms are based on the Adaptive Quantisation Coding (AQC) algorithm, and they achieve a controllable output bit rate and accuracy by considering the intensity variation of each image block. Their high speed, low hardware usage and low power consumption architectures are also introduced and implemented on Xilinx devices. Furthermore, efficient hardware architectures for multidimensional DCT based on the 1-D DCT Radix-2 and 3-D DCT Vector Radix (3-D DCT VR) fast algorithms have been proposed. These architectures attain fast and accurate 3-D DCT computation and provide high processing speed and power consumption reduction. In addition, this research also introduces two low hardware usage 3-D DCT VR architectures. Such architectures perform the computation of butterfly and post addition stages without using block memory for data transposition, which in turn reduces the hardware usage and improves the performance of the proposed architectures. Moreover, parallel and multiplierless lifting-based architectures for the 1-D, 2-D and 3-D Cohen-Daubechies-Feauveau 9/7 (CDF 9/7) DWT computation are also introduced. The presented architectures represent an efficient multiplierless and low memory requirement CDF 9/7 DWT computation scheme using the separable approach. Furthermore, the proposed architectures have been implemented and tested using Xilinx FPGA devices. The evaluation results have revealed that a speed of up to 315 MHz can be achieved in the proposed AQC-based architectures. Further, a speed of up to 330 MHz and low utilisation rate of 722 to 1235 can be achieved in the proposed 3-D DCT VR architectures. In addition, in the proposed 3-D DWT architecture, the computation time of 3-D DWT for data size of 144×176×8-pixel is less than 0.33 ms. Also, a power consumption of 102 mW at 50 MHz clock frequency using 256×256-pixel frame size is achieved. The accuracy tests for all architectures have revealed that a PSNR of infinite can be attained

Development of Low Power Image Compression Techniques

Digital camera is the main medium for digital photography. The basic operation performed by a simple digital camera is, to convert the light energy to electrical energy, then the energy is converted to digital format and a compression algorithm is used to reduce memory requirement for storing the image. This compression algorithm is frequently called for capturing and storing the images. This leads us to develop an efficient compression algorithm which will give the same result as that of the existing algorithms with low power consumption. As a result the new algorithm implemented camera can be used for capturing more images then the previous one. 1) Discrete Cosine Transform (DCT) based JPEG is an accepted standard for lossy compression of still image. Quantisation is mainly responsible for the amount loss in the image quality in the process of lossy compression. A new Energy Quantisation (EQ) method proposed for speeding up the coding and decoding procedure while preserving image qu..

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Hardware realization of discrete wavelet transform cauchy Reed Solomon minimal instruction set computer architecture for wireless visual sensor networks

Large amount of image data transmitting across the Wireless Visual Sensor Networks (WVSNs) increases the data transmission rate thus increases the power transmission. This would inevitably decreases the operating lifespan of the sensor nodes and affecting the overall operation of WVSNs. Limiting power consumption to prolong battery lifespan is one of the most important goals in WVSNs. To achieve this goal, this thesis presents a novel low complexity Discrete Wavelet Transform (DWT) Cauchy Reed Solomon (CRS) Minimal Instruction Set Computer (MISC) architecture that performs data compression and data encoding (encryption) in a single architecture. There are four different programme instructions were developed to programme the MISC processor, which are Subtract and Branch if Negative (SBN), Galois Field Multiplier (GF MULT), XOR and 11TO8 instructions. With the use of these programme instructions, the developed DWT CRS MISC were programmed to perform DWT image compression to reduce the image size and then encode the DWT coefficients with CRS code to ensure data security and reliability. Both compression and CRS encoding were performed by a single architecture rather than in two separate modules which require a lot of hardware resources (logic slices). By reducing the number of logic slices, the power consumption can be subsequently reduced. Results show that the proposed new DWT CRS MISC architecture implementation requires 142 Slices (Xilinx Virtex-II), 129 slices (Xilinx Spartan-3E), 144 Slices (Xilinx Spartan-3L) and 66 Slices (Xilinx Spartan-6). The developed DWT CRS MISC architecture has lower hardware complexity as compared to other existing systems, such as Crypto-Processor in Xilinx Spartan-6 (4828 Slices), Low-Density Parity-Check in Xilinx Virtex-II (870 slices) and ECBC in Xilinx Spartan-3E (1691 Slices). With the use of RC10 development board, the developed DWT CRS MISC architecture can be implemented onto the Xilinx Spartan-3L FPGA to simulate an actual visual sensor node. This is to verify the feasibility of developing a joint compression, encryption and error correction processing framework in WVSNs

Hardware realization of discrete wavelet transform cauchy Reed Solomon minimal instruction set computer architecture for wireless visual sensor networks

Large amount of image data transmitting across the Wireless Visual Sensor Networks (WVSNs) increases the data transmission rate thus increases the power transmission. This would inevitably decreases the operating lifespan of the sensor nodes and affecting the overall operation of WVSNs. Limiting power consumption to prolong battery lifespan is one of the most important goals in WVSNs. To achieve this goal, this thesis presents a novel low complexity Discrete Wavelet Transform (DWT) Cauchy Reed Solomon (CRS) Minimal Instruction Set Computer (MISC) architecture that performs data compression and data encoding (encryption) in a single architecture. There are four different programme instructions were developed to programme the MISC processor, which are Subtract and Branch if Negative (SBN), Galois Field Multiplier (GF MULT), XOR and 11TO8 instructions. With the use of these programme instructions, the developed DWT CRS MISC were programmed to perform DWT image compression to reduce the image size and then encode the DWT coefficients with CRS code to ensure data security and reliability. Both compression and CRS encoding were performed by a single architecture rather than in two separate modules which require a lot of hardware resources (logic slices). By reducing the number of logic slices, the power consumption can be subsequently reduced. Results show that the proposed new DWT CRS MISC architecture implementation requires 142 Slices (Xilinx Virtex-II), 129 slices (Xilinx Spartan-3E), 144 Slices (Xilinx Spartan-3L) and 66 Slices (Xilinx Spartan-6). The developed DWT CRS MISC architecture has lower hardware complexity as compared to other existing systems, such as Crypto-Processor in Xilinx Spartan-6 (4828 Slices), Low-Density Parity-Check in Xilinx Virtex-II (870 slices) and ECBC in Xilinx Spartan-3E (1691 Slices). With the use of RC10 development board, the developed DWT CRS MISC architecture can be implemented onto the Xilinx Spartan-3L FPGA to simulate an actual visual sensor node. This is to verify the feasibility of developing a joint compression, encryption and error correction processing framework in WVSNs

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering